Preventing Failures by Predicting Students' Grades through an Analysis of Logged Data of Online Interactions

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Cabral, B (Autor) Outra; Figueira, A (Autor) FCUP

Ata de Conferência Internacional

Título: Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Páginas: 491-499

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00R-3H7

DOI: 10.5220/0008356604910499

Abstract (EN): Nowadays, students commonly use and are assessed through an online platform. New pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be intense feedback from these activities to students, usually it is restricted to the assessments of the online set of tasks. We propose a model that informs students of abnormal deviations of a ¿correct¿ learning path. Our approach is based on the vision that, by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student¿s current online actions towards the course. In the major learning management systems available, the interaction between the students and the system, is stored in log. Our proposal uses that logged information, and new one computed by our methodology, such as the time each student spends on an activity, the number and order of resources used, to build a table that a machine learning algorithm can learn from. Results show that our model can predict with more than 86% accuracy the failing situations. Copyright

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9